Data Visualisation

1. HCI 0283 Lecture 4 Representation Data Visualisation

2. Symbolic Encoding • Last lecture we looked at how to represent numeric data as points in a multidimensional space • Although this is useful in many cases we often need to represent ordinal and categorical data as well as numerical data • We also need to consider other ways of representing numerical data • We have already seen some encoding methods • Line thickness in Minard’s map • Line colour in Beck’s Underground Diagram • Slope in Playfair’s tax diagrams • There are many other possible encoding schemes and no complete theory to guide us on what to use when • We will therefore look at many possible methods of encoding data…

3. Size • In the MINNIE computer-aided design system size is used to show the relative influence of each item in a circuit diagram • We are not too concerned about the actual value – 5.1,5.2 or 5.3 – but more whether the effect is small, medium or large • If necessary we can show the actual value in a separate display

4. PROPERTIES FREQ DOMAIN LOG FREQ SCAN FROM 1 HZ TO 95.77 HZ SENSITIVITY HIGH FREQ: 74.8 BASS TREBLE

5. Length and Height • Encoding numerical data by length and height is popular when a qualitative feel for the data is required • Mosaic plots are an example of this • Another is the use of dynamic bar graphs – the height of the bar represents the current value of that particular control • Graphic equaliser / amplifier displays

6. Length and Height • It is possible to create a control that allows the viewer to switch quickly between a qualitative and quantitative interpretation • Aircraft altimeter 2200 2000 18 20 1800 00 1600 1400 STOP 1200 1000

7. Magnification • The simplest way to use magnification is to make the symbols representing items with a larger value appear physically larger than those for items with smaller values • A more complex method relies on people having a good mental image of an object then distorting the visualisation in some way to make items with larger values more obvious • We can use magnification of areas to encode geographical data by distorting the size and shape of a country to reflect its population, energy consumption, greenhouse gas emissions… • These are complex and only created using computers

8. Normal Map of the World http://www-personal.umich.edu/~mejn/cartograms/

9. World Population Cartogram http://www-personal.umich.edu/~mejn/cartograms/

10. Energy Consumption Cartogram http://www-personal.umich.edu/~mejn/cartograms/

11. Greenhouse Gas Emission Cartogram http://www-personal.umich.edu/~mejn/cartograms/

12. Faces • Human beings are very good at identifying facial expressions • Chernoff suggested using this as a coding method using features such as the size of the eyes, the shape of the mouth, the size and height of the eyebrows above the eyes, the shape of the face etc • He then used these to encode data about 18 attributes of geological samples such as salt content and water content

13. Faces • 10 Parameters: • Head Eccentricity • Eye Eccentricity • Pupil Size • Eyebrow Slope • Nose Size • Mouth Vertical Offset • Eye Spacing • Eye Size • Mouth Width • Mouth Openness • http://hesketh.com/schampeo/projects/Faces/chernoff.html

14. Multidimensional Icons • Chernoff faces are examples of multidimensional icons • We can have other multidimensional icons for other domains • Spence and Parr developed one for rapid identification and selection of houses satisfying various requirements • Colour – cost (white low, red high) • Shape – house, flat/apartment, houseboat • Bedrooms – number of windows • State of repair – good=white, black=bad • Travel time – clock icon • Garden size – sized icon • Central heating, garage – icons • Eight attributes in total

15. ◑ ≋ ◔ ≋ ◔ ⍝ Houseboat £200,000 No garage 3 bedrooms No central heating Good repair No garden 15 minutes to station House £400,000 Garage 4 bedrooms Central heating Good repair Large garden 15 minutes to station Apartment £300,000 No garage 6 bedrooms Central heating Poor repair Large garden 30 minutes to station

16. Multidimensional Icons • These are very good for solving problems such as • You can spend up to £300,000 one accommodation but need somewhere with central heating and at least two bedrooms • Find the cheapest place you can with 3 bedrooms within 15 minutes of the station • One significant advantage is that the icons look like the things they represent, unlike faces and geological data • In general when using icons try to make them look like the thing they represent, as familiar associations make it easier for the viewer to recognise and remember them

17. Spatiality • If you are arranging the books on your bookshelves you will probably do it either by alphabetical order of author or by subject • The writer Alastair Cooke organised his by location – books on Florida were in the bottom right, books on Texas in the bottom left, and books on California at waist height on the left • Human beings generally have excellent spatial memory • Even if your desk looks a mess you probably still have an idea where everything is – an internal model • Different piles of paper on a desk have different significances • Visualisation using spatial internal models is probably the oldest memory aid (Plato)

18. Coffee Time!

19. Patterns • Patterns are a valuable encoding technique particularly when combined with other techniques • Humans are generally very good at picking out patterns…

22. Patterns • Humans are particularly good at filling in the gaps to see patterns • Suppose a company begins to produce a new piece of hardware and notes the failure rate in terms of months in service (MIS) and month of production (MOP) • Can this tell us anything about the production process?

24. 2 7 8 3 6 1: Running in production 2: End of guarantee period 3: Running out production 4: Latent defects 5: Early wearout, ageing 6: Seasonal influence 7: Epidemical failure 8: Learning curve 1 1 2 3 4 5 6 7 8 9 10 11 12 Month in Service 5 4 1 2 3 4 5 6 7 8 9 10 11 12 Month of Production

25. Patterns • Human pattern recognition is also useful in social interaction • Current internet chat environments do not show many social clues • For instance, someone listening to a discussion (lurking) is invisible and so may feel that they need to post a ‘hello’ message

26. Chat Circles John Anna Freyja Jenny No, I was at the SigurRós concert Did you see Survivor on TV last night? Tom Quiet night in Ivan WOW! Steve

27. Chat Circles • A related tool allows us to visualise the archives of a chat session • We can see who dominates the conversation and who lurks Quiet night in

28. Sound • Visualisation does not need to be visual • Aural presentation can be used on its own or in conjunction with visual forms • There are many different features of sound that may be used to encode information • Instrument, pitch, tempo, rhythm, chords • The presentation can range from a beep to a large-scale soundscape

29. Virtual Worlds • When you visit a web site such as Google you know intellectually that there are thousands of other people visiting the site at the same time • You have no idea who these people are, nor can you interact with them • Virtual worlds allow people to interact within a 3D environment across the web • By far the most successful virtual worlds are games such as Everquest and World of Warcraft • Within such worlds the player must have a physical body or avatar which can interact with other avatars • These avatars can be designed in great detail by the players

30. Colour • Colour is a useful encoding tool • It must, however, be used with great care for cultural and physiological reasons • The minimum size of a point or the thickness of a line that can easily be discriminated differs with differing colours • Colour is often used to show scales, e.g. spectral, shaded or bi-colour

31. Colours • The human eye can distinguish over 10 million different colours under suitable viewing conditions • 13 colours • Chromatics: red, orange, yellow, green, blue, violet, purple, pink, brown, olive • Achromatics: black, white, grey • Brown and olive are not usually included in the basic colour terms

32. Spectral Coding

33. Shaded Coding

34. Bi-colour Coding

35. Which Is Best? • It depends… • We don’t know… • There are no hard and fast rules on when to use which technique • Multiple techniques are often used together • A visualisation tool that allowed the designer to choose from all of these would be exceedingly complex

36. Different types of stimuli

37. Different types of stimuli

38. Summary • There are many different ways of representing – encoding – data • Symbols, size, length, magnification, faces, multidimensional icons, spatial arrangement, pattern, sound, colour… • The ‘best method’ will depend on the task to be performed with the data • Automating this task would require a very complex expert system

39. Coming Soon… • Next lecture: Dynamic Rearrangement • Homework: Read chapter 4 of Information Visualisation (Spence) and the papers handed out in the lecture